Research On Semantic-based Web Image Classification

Nowadays, with digital cameras and mass storage devices becoming increasingly affordable, each day thousands of pictures are taken and multimedia archives on the Internet are growing at an astonishing rate. Besides, image search engines such as Yahoo! and Google, and photo management tools such as Flickr, making image resource available on the Internet. People can easily build personal digital picture collections and online photo sharing has become a common practice. However, for representing, indexing and retrieving pictures on the Internet efficiently, it is necessary and essential to analyze the image content and reveal the semantic information of the images. Semantic-based image classification is an effective way to discover valuable information from a huge number of Web images, having great prospect of applications. At present, the technique of extracting low-level image feature is relative maturity, but the effectiveness of obtaining high-level semantic imformation fails to satisfy the demands of practical applications. Moreover, Web images usually have the characteristics of vast quantity, high dimensionality, and nonlinear structure, and they are also rich in contents. Therefore, semantic-based Web image classification is not only an urgent problem, but also a challenging and rewarding project.This dissertation is aimed to provide a solution to the problem of semantic-based Web image classification. We also have achieved a certain innovative results. The main contributions are summarized as follows.(1) In order to shorten the "semantic gap" between low-level image feature and high-level semantic information, firstly, according to the different abstract degrees of semantic information, we set up a three-layer image semantic model. And then, some representative expression approaches of semantic information for describing image contents are dicussed, based on the proposed model. Finally, with a view of analyzing the characteristics of Web images, we make an intensive study of the causes of the differences between different Web images, which is a premise of developing an efficient strategy for Web image classification. (2) Image feature optimization is an important means to deal with high-dimensional image in Web image classification. Image feature optimization can be formulated via a five-tuple model. We propose a globular neighborhood based locally linear embedding (GNLLE) by using neighborhood update and an incremental neighbor search, which not only can handle sparse datasets but also has strong anti-noise capability and good topological stability. Given that the distance measure adopted in nonlinear dimensionality reduction is usually based on pairwise similarity calculation, we also present a globular neighborhood and path clustering based locally linear embedding (GNPCLLE). Due to its full consideration of the correlations between data, GNPCLLE can eliminate the distortion of the overall topological structure within the dataset. Experimental results on two Web image sets show the feasibility and effectiveness of both GNLLE and GNPCLLE.(3) Segmenting region of interest (ROI) from Web images accurately is important for improving the performance of Web image classification. A novel image segmentation strategy is proposed, which consists of a rough segmentation stage and a fine segmentation stage. In the first stage, an image is partitioned into four regions by using a block clustering based on color and texture features, and the ROI within the image is distinguished from the background according to the principles of photographic composition. This stage aims to determine the approximate region of the target. In the second stage, a novel active contour model is established based on shape information and vector method, where the image energy is defined by a hue gradient and the external energy is generated from both triangular inner force and supplementary force. This stage tries to extract the boundary of the target accurately. Experiments are conducted on Web images to validate the effectiveness of the proposed image segmentation method.(4) The batch-based processing for classifying a set of image sets has been paid more and more attentions. In order to improve the classification accuracy of Web images, we propose a novel hierarchical model based on different granularity levels of image smenatic. In which two kinds of nonlinear manifolds:multi-category object manifold and one-category scene manifold are formulated. The multi-category object manifold is constructed in object-level classification by an extended locally linear embedding (ELLE) algorithm, taking into account both the intra-category and the inter-category discriminations among the images belonging to different semantic categories. During scene-level classification, the one-category scene manifold is set up for the images corresponding to the same object category but having several different scenes. A locally linear sub-manifold establishment (LLSE) algorithm is developed via region growing and linear perturbation. The performance of the proposed object-oriented hierarchical classification model is tested on two Web image sets.(5) As for the complexity of Web image classification, a novel classification method based on double manifold learning is proposed, transforming image classification in high-dimensional space to a lower dimensional feature space. At first, two low-dimensional nonlinear manifolds with different intrinsic dimensionalities are established respectively, according to the significant differences between the positive images and the negative images. And then, each aggregation center of the manifolds is calculated on the basis of the grouping characteristics in GLLE. At last, a classifier is constructed on the distance measure based on double manifold, and it is applied in global-based image classification, having an advantage of reflecting the data topological structure correctly. Experiments on Web images indicate that the proposed image classification method is efficient.